scholarly journals Optimization of Computer Numerical Control Interpolation Parameters Using a Backpropagation Neural Network and Genetic Algorithm with Consideration of Corner Vibrations

2021 ◽  
Vol 11 (4) ◽  
pp. 1665
Author(s):  
Hsiang-Chun Tseng ◽  
Meng-Shiun Tsai ◽  
Chih-Chun Cheng ◽  
Chen-Jung Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Performance Index ◽  
Correlation Coefficients ◽  
Computer Numerical Control ◽  
Numerical Control ◽  
Backpropagation Neural Network ◽  
Validation Data ◽  
Position Errors ◽  
Interpolation Parameter

This paper presents an optimization algorithm for tuning the interpolation parameters of computer numerical control (CNC) controllers; it operates by considering multiple objective functions, namely, contour errors, the machining time (MT), and vibrations. The position commands, position errors, and vibration signals from 1024 experiments were considered in the designed trajectory. The experimental data—the maximum contour error (MCoE), MT, and corner vibration (CVib)—were analyzed to compute the performance index. A backpropagation neural network (BPNN) with 20 hidden layers was applied to predict the performance index. The correlation coefficients for the predicted values and experimental results for the MCoE, MT, and CVib based on the validation data were 0.9984, 0.9998, and 0.9354, respectively. The high correlation coefficients highlight the accuracy of the model for designing the interpolation parameter. After the BPNN model was developed, a genetic algorithm (GA) was adopted to determine the optimized parameters of the interpolation under different weighting of the performance index. A weighted sum approach involving the objective function was employed to determine the optimized interpolation parameters in the GA. Thus, operators can judge the feasibility of the interpolation parameter for various weighting settings. Finally, a mixed path was selected to verify the proposed algorithm.

The Application of Genetic Algorithm Backpropagation Neural Network Model on the Prediction and Optimization of Wastewater Treatment System

2013 ◽  
Vol 838-841 ◽  
pp. 2525-2531
Author(s):  
Fu Quan Jia ◽  
Zhang Wei He ◽  
Zhu Jun Tian ◽  
Zhao Bo Chen ◽  
Hong Cheng Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Wastewater Treatment ◽  
Network Model ◽  
Neural Network Model ◽  
Correlation Coefficients ◽  
Treatment System ◽  
Backpropagation Neural Network ◽  
Predictive Values ◽  
Wastewater Treatment System

Prediction and optimization on water quality parameters (WQPs) have become more and more important to the wastewater treatment system (WWTs). In this study, the genetic algorithm backpropagation neural network model (GA-BPNN) had been used to predict and optimize WQPs of a low-strengthen complex wastewater treatment system (LSCWWTs). Results showed that the correlation coefficients between the predicted values and measured values were R2 =0.946 for COD, R2=0.962 for BOD, R2=0.933 for TN, R2=0.985 for NH3-N, R2=0.969 for TP, and R2=0.968 for SS, indicating the predictive values by the GA-BPNN model well fitted the mesured values of effluent WQPs. The optimal effluent WQPs were COD=27.6mg/L, BOD=7.1mg/L, TN=5.4mg/L, NH3-N=0.9mg/L, TP=0.11mg/L and SS=9.25mg/L, respectively. And the corresponding operating parameters were MLSS=3045.4mg/L, MLVSS=2405.9mg/L, T=23.2 °C, R=1.4, SRT=12.5d, HRT=17.3h, CODin =643.3mg/L, BODin=342.2mg/L, TNin=54.2mg/L, NH3-Nin=45.3mg/L, TPin=4.9mg/L, SSin=452.6mg/L, which could be beneficial to the operation optimization of LSCWWTs.

Emulating and Modeling for Position Errors of Ultra-Precision Aspherical Grinding

2007 ◽  
Vol 10-12 ◽  
pp. 291-296
Author(s):  
Dong Ju Chen ◽  
Yong Zhang ◽  
Fei Hu Zhang ◽  
H.M. Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Motor Coordination ◽  
Error Model ◽  
Position Error ◽  
Numerical Control ◽  
Training Data ◽  
Position Errors ◽  
The Neural Network ◽  
Ultra Precision

In the process of the ultra-precision grinding, the machining path of the aspherical is the result of motor coordination by several axes for the numerical control system. Since the motion of each axis have errors, there are big errors between the real positions and the theoretical positions, and the position error of the wheel infects the accuracy of the workpiece greatly. This paper analyses the position error property of the wheel and finds the machining approach path has nothing to do with the position error, just do with to the present machining point. In order to solve the problem, the method using the Neural Network optimized by the Genetic Algorithm to establish the position error model is introduced. A three-layer error back propagation (simplified as BP) Neural Network is used to establish the position error model, the position coordinates (x, z) of the program instruction is input layer, and the corroding measured error value ( Δx , Δz ) is output layer. Before training data sample, using the Genetic Algorithm to optimize the Neural Network to improve the predicting accuracy of the Neural Network, and reduce the training time. The emulation results indicate that using the Neural Network model optimized by the Genetic Algorithm can predict the position error in a high degree of accuracy, and at the same time, according to the predicting results, compensating the position error of the wheel is possible.

scholarly journals Intrusion Detection Method Based on Adaptive Clonal Genetic Algorithm and Backpropagation Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yi Lu ◽  
Menghan Liu ◽  
Jie Zhou ◽  
Zhigang Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Detection Rate ◽  
Detection System ◽  
Poor Performance ◽  
Data Sets ◽  
Backpropagation Neural Network ◽  
External Threats ◽  
Kdd Cup 99

Intrusion Detection System (IDS) is an important part of ensuring network security. When the system faces network attacks, it can identify the source of threats in a timely and accurate manner and adjust strategies to prevent hackers from intruding. Efficient IDS can identify external threats well, but traditional IDS has poor performance and low recognition accuracy. To improve the detection rate and accuracy of IDS, this paper proposes a novel ACGA-BPNN method based on adaptive clonal genetic algorithm (ACGA) and backpropagation neural network (BPNN). ACGA-BPNN is simulated on the KDD-CUP’99 and UNSW-NB15 data sets. The simulation results indicate that, in contrast to the methods based on simulated annealing (SA) and genetic algorithm (GA), the detection rate and accuracy of ACGA-BPNN are much higher than of GA-BPNN and SA-BPNN. In the classification results of KDD-CUP’99, the classification accuracy of ACGA-BPNN is 11% higher than GA-BPNN and 24.2% higher than SA-BPNN, and F-score reaches 99.0%. In addition, ACGA-BPNN has good global searchability and its convergence speed is higher than that of GA-BPNN and SA-BPNN. Furthermore, ACGA-BPNN significantly improves the overall detection performance of IDS.

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